A compilation of soybean ESTs: generation and analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Whole-genome sequencing is fundamental to understanding the genetic composition of an organism. Given the size and complexity of the soybean genome, an alternative approach is targeted random-gene sequencing, which provides an immediate and productive method of gene discovery. In this study, more than 120000 soybean expressed sequence tags (ESTs) generated from more than 50 cDNA libraries were evaluated. These ESTs coalesced into 16928 contigs and 17336 singletons. On average, each contig was composed of 6 ESTs and spanned 788 bases. The average sequence length submitted to dbEST was 414 bases. Using only those libraries generating more than 800 ESTs each and only those contigs with 10 or more ESTs each, correlated patterns of gene expression among libraries and genes were discerned. Two-dimensional qualitative representations of contig and library similarities were generated based on expression profiles. Genes with similar expression patterns and, potentially, similar functions were identified. These studies provide a rich source of publicly available gene sequences as well as valuable insight into the structure, function, and evolution of a model crop legume genome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it